International commercial arbitration in Canada after <i>Uber Technologies Inc v Heller</i>
Bibliographic record
Abstract
Abstract This article examines the Supreme Court of Canada’s judgment in Uber Technologies Inc v Heller from an international commercial arbitration perspective, focusing on two specific issues. The first issue is the Court’s application of a provincial domestic, rather than international, arbitration statute to Uber and Heller’s international arbitration agreement, on the ground that the agreement is not ‘commercial’. The article argues that this finding is not in line with international arbitration instruments and practice. The second issue is the Court’s interpretation and application of the competence–competence principle, which permits arbitral tribunals to decide their own jurisdiction. The article maintains that the Court’s approach does not offend this principle, but that the Court provides insufficient guidance to lower Canadian courts on how to implement this approach in future cases. The article concludes that the Court’s decision, while far-reaching in many respects, should not disturb the enforcement of routine international commercial arbitration agreements in Canada. The decision may have implications, however, for arbitration agreements contained in international contracts—particularly standard form contracts—that might give rise to employment disputes, such as those in the gig economy, or that contain terms that may seem to bar a party’s access to the arbitral process.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.005 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.020 | 0.006 |
| Scholarly communication | 0.012 | 0.001 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.007 | 0.009 |
| Insufficient payload (model declined to judge) | 0.007 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".